Tag Recommendations in Folksonomies
By Robert Jäschke, Leandro Bezerra Marinho, Andreas Hotho, Lars Schmidt-Thieme, Gerd Stumme. LWA, 2007
609 citations recorded by the source corpus.
Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition, Information Systems
View Paper: https://www.semanticscholar.org/paper/24e09e7269d8b8dab9bda342ec1a8716e9bb32ac
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Artificial Intelligence
- Lars Schmidt-Thieme
- Steffen Rendle
- Gerhard Weikum
- Xiangnan He
- Fabian M. Suchanek
- Martin Wistuba
- Lucas Drumond
- John Riedl
In Computer Vision and Pattern Recognition
- John Riedl
- Lars Schmidt-Thieme
- J. Konstan
- Hongwei Wang
- Junlin Zhang
- Martin Wistuba
- Josif Grabocka
- Lucas Drumond
In Information Systems
- Charu C. Aggarwal
- Steffen Rendle
- Xiangnan He
- Lars Schmidt-Thieme
- John Riedl
- J. Konstan
- Tat-seng Chua
- Yongfeng Zhang
Citations in the corpus
- Latent dirichlet allocation for tag recommendation (2009, methodology): “[20] compare two variants of collaborative filtering and Folkrank, a graph based algorithm for personalized tag recommendation.”
- Pairwise interaction tensor factorization for personalized tag recommendation (2010, background): “In .gure 8, a comparison to BPR-CD, RTF-TD, Folkrank, Pagerank and HOSVD on Bibsonomy and Last.fm is shown.”
- Tagommenders: connecting users to items through tags (2009, neutral): “Although previous researchers have investigated the tag inference problem [13], they have not made use of patterns in users’ ratings of items.”
- Tag recommendations based on tensor dimensionality reduction (2008, neutral): “[10], considered two alternative 2-dimensional projections to model the relationship among users, items and tags, in order to apply CF in social tagging.”
- Recommender systems: from algorithms to user experience (2012, methodology): “For instance, Jaschke et al. (2007) propose a graph-based algorithm based on PageRank, which they find does better than previously proposed algorithms.”
- Personalized Deep Learning for Tag Recommendation (2017, methodology): “It is also compared to the non-personalized models including most popluar tags(MP) [6], the multilabel neural networks (BP-MLLs) [24] that have low-level visual features as the input (CH-BPMLL, CM55-BPMLL), CNNs obtained for image annotation which optimizes the pairwise ranking loss to learn the par”
- YAGO: A Large Ontology from Wikipedia and WordNet (2008, background): “Similar observations hold for the recently popularized direction of mining taxonomies and semantic relations from social-tagging platforms such as del.icio.us and Web directories such as dmoz.org (see, e.g., [22] [31] [23]).”
- We know what @you #tag: does the dual role affect hashtag adoption? (2012, background): “The factors affecting content tagging are inspired from the studies of traditional content tagging systems [25, 15], especially tag recommender systems [20, 14].”
- Learning optimal ranking with tensor factorization for tag recommendation (2009, methodology): “The BibSonomy dataset we used is available from the University of Kassel [4].”
- Recommender systems survey (2013, background): “[110], the authors compare different mechanisms for tags recommendations.”
- Evaluating similarity measures for emergent semantics of social tagging (2009, neutral): “[48, 24]), and combinations of the two [21].”
- Collaborative Filtering-Based Recommendation of Online Social Voting (2017, background): “Many OSNs now offer the social voting function, through which a user can share with friends her opinions, e.g., like or dislike, on various subjects, ranging from user statuses, profile pictures, to games played, Manuscript received October 22, 2014; revised August 19, 2016; accepted January 24,…”